UCI Adult Income Dataset - Exploratory and Descriptive Analysis

Author

Affiliation

Nyiramana Annick

Junior Data Analyst

In this notebook, we carry out an in-depth exploratory and descriptive analysis of the UCI Adult Income Dataset, a widely used dataset for income prediction tasks based on individual demographic and employment attributes.

This phase of analysis is essential for uncovering patterns, detecting potential biases, and gaining intuition about the dataset’s structure before applying any modelling procedures. We examine the distribution of key numerical and categorical variables, investigate relationships between demographic features and income levels, and use visualizations to summarize insights. Particular focus is placed on income disparities across **age groups**, **geographical regions**, **races**, and **education-occupation combinations**, helping lay a solid foundation for downstream modeling and policy-relevant interpretation.

We begin our analysis by importing the core Python libraries required for **data handling**, **numerical computation**, **visualization**, and **directory management**:

- `pandas`: Enables efficient manipulation, filtering, and aggregation of structured tabular data, forming the backbone of our analysis pipeline.

- `numpy`: Provides support for fast numerical operations, array-based computation, and statistical routines.

- `os`: Facilitates interaction with the file system, allowing us to construct flexible and portable directory paths for data and output management.

- `plotly.express`: A high-level graphing library that enables the creation of interactive, publication-quality visualizations, which we use extensively to uncover patterns and present insights throughout the notebook.

# Import libraries 
import os
import pandas as pd
import numpy as np
import plotly.express as px

import plotly.io as pio
pio.renderers.default = 'notebook'

Define and Create Directory Paths

To ensure reproducibility and organized storage, we programmatically create directories if they don’t already exist for:

- raw data
- processed data
- results
- documentation

These directories will store intermediate and final outputs for reproducibility.

# Get working directory 
current_dir = os.getcwd()

# Go one directory up to the root directory 
project_root_dir = os.path.dirname(current_dir)
data_dir = os.path.join(project_root_dir, 'data')
raw_dir = os.path.join(data_dir,'raw')
processed_dir = os.path.join(data_dir,'processed')
# Define paths to results folder 
results_dir = os.path.join(project_root_dir,'results')
# Define paths to docs folder 
docs_dir = os.path.join(project_root_dir,'docs') 

#Create directories if they do not exist 
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(raw_dir,exist_ok= True)
os.makedirs(results_dir, exist_ok=True)
os.makedirs(processed_dir, exist_ok=True)

Loading the Cleaned Dataset

We load the cleaned version of the UCI Adult Income Dataset from the processed data directory into a Pandas DataFrame. The `head(10)` function shows the first ten records, giving a glimpse into the data columns such as `age`, `workclass`, `education_num`, etc.

adult_data_filename= os.path.join(processed_dir, "adult_cleaned.csv")
adult_df = pd.read_csv(adult_data_filename)
adult_df.head(10)

	age	workclass	fnlwgt	education_num	marital_status	relationship	race	sex	capital_gain	capital_loss	hours_per_week	income	education_level	occupation_grouped	native_region	age_group
0	39	government	77516	13	single	single	white	male	2174	0	40	<=50k	tertiary	white collar	north america	36-45
1	50	self-emp-not-inc	83311	13	married	male spouse	white	male	0	0	13	<=50k	tertiary	white collar	north america	46-60
2	38	private	215646	9	divorced or separated	single	white	male	0	0	40	<=50k	secondary-high school graduate	blue collar	north america	36-45
3	53	private	234721	7	married	male spouse	black	male	0	0	40	<=50k	secondary	blue collar	north america	46-60
4	28	private	338409	13	married	female spouse	black	female	0	0	40	<=50k	tertiary	white collar	central america	26-35
5	37	private	284582	14	married	female spouse	white	female	0	0	40	<=50k	tertiary	white collar	north america	36-45
6	49	private	160187	5	divorced or separated	single	black	female	0	0	16	<=50k	secondary	service	central america	46-60
7	52	self-emp-not-inc	209642	9	married	male spouse	white	male	0	0	45	>50k	secondary-high school graduate	white collar	north america	46-60
8	31	private	45781	14	single	single	white	female	14084	0	50	>50k	tertiary	white collar	north america	26-35
9	42	private	159449	13	married	male spouse	white	male	5178	0	40	>50k	tertiary	white collar	north america	36-45

Dataset Dimensions and Data Types

Here, we examine the structure of the dataset:

- There are *32,513* entries and *16* variables.
- The dataset includes both **numerical** (e.g., `age`, `hours_per_week`) and **categorical** variables (e.g., `sex`, `education_level`).

Understanding data types and null entries is essential before proceeding with analysis.

adult_df.shape

(32513, 16)

adult_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32513 entries, 0 to 32512
Data columns (total 16 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   age                 32513 non-null  int64 
 1   workclass           32513 non-null  object
 2   fnlwgt              32513 non-null  int64 
 3   education_num       32513 non-null  int64 
 4   marital_status      32513 non-null  object
 5   relationship        32513 non-null  object
 6   race                32513 non-null  object
 7   sex                 32513 non-null  object
 8   capital_gain        32513 non-null  int64 
 9   capital_loss        32513 non-null  int64 
 10  hours_per_week      32513 non-null  int64 
 11  income              32513 non-null  object
 12  education_level     32513 non-null  object
 13  occupation_grouped  32513 non-null  object
 14  native_region       32513 non-null  object
 15  age_group           32513 non-null  object
dtypes: int64(6), object(10)
memory usage: 4.0+ MB

Summary Statistics: Numerical Variables

This summary provides a snapshot of key distribution characteristics. We see that:

- Age ranges from 17 to 90, with a mean of 38.6 years. It is slightly right-skewed (positively skewed). While the average age is approximately 38.6 years, an examination of the percentiles reveals that the majority of individuals are clustered in the younger to middle-age range, with fewer observations in the older age brackets. This skewed age distribution might suggest labor force participation is concentrated in specific age groups, which could reflect broader demographic or economic realities.

- Capital gains/losses are highly skewed, with most values at 0 (the 75th percentile is 0). This indicates that a small number of individuals report very large gains or losses, especially evident in the capital gain variable which reaches up to $99,999. These variables act as proxies for wealth-related income that goes beyond regular wages or salaries. Individuals with non-zero values for capital gains or losses often represent a distinct socioeconomic subset of the population — typically more financially literate, or with access to investment assets. The stark inequality in their distributions mirrors real-world disparities in asset ownership and investment returns.

- The dataset has individuals working anywhere from 1 to 99 hours per week, with a median of 40. This aligns with the standard full-time work week in many countries (8 hours per day for 5 working days). The mean is slightly above that at 40.4 hours, suggesting a mild right skew, with a small subset of individuals working significantly longer hours. The mode is also 40, further reinforcing the prevalence of full-time work. A non-trivial number of individuals report working very few hours, possibly due to part-time work, unemployment, or semi-retirement. On the other extreme, some report working more than 45 hours per week, which may indicate multiple jobs, weekend-work, self-employment, or informal labor, and could reflect socioeconomic necessity.

adult_df.describe()

	age	fnlwgt	education_num	capital_gain	capital_loss	hours_per_week
count	32513.000000	3.251300e+04	32513.000000	32513.000000	32513.000000	32513.000000
mean	38.590256	1.897942e+05	10.081629	1079.239812	87.432719	40.440962
std	13.638932	1.055788e+05	2.572015	7390.625650	403.243596	12.350184
min	17.000000	1.228500e+04	1.000000	0.000000	0.000000	1.000000
25%	28.000000	1.178330e+05	9.000000	0.000000	0.000000	40.000000
50%	37.000000	1.783560e+05	10.000000	0.000000	0.000000	40.000000
75%	48.000000	2.370510e+05	12.000000	0.000000	0.000000	45.000000
max	90.000000	1.484705e+06	16.000000	99999.000000	4356.000000	99.000000

Summary Statistics: Categorical Variables

`workclass`

The private sector dominates, employing ~69.7% of the population. The government sector (13.4%) and self-employment (11.2%) also make up substantial portions of the workforce. A small fraction is labeled as “unknown” (5.6%), which may correspond to missing or ambiguous data entries. Tiny proportions are voluntary (0.04%) or unemployed (0.02%), possibly underreported or underrepresented in the sample.

`marital_status`

Married individuals make up the largest group (46.1%), followed by those who are single (32.8%) and divorced or separated (18.1%). Widowed individuals represent a small minority (~3.1%).

`relationship`

The majority are labeled as “male spouse” (40.5%) or “single” (36.1%). Smaller categories include children (15.6%), female spouses (4.8%), and extended relatives (3.0%). The dominance of `male spouse` reflects the dataset’s gendered structure and may point to traditional family roles. The relative scarcity of “female spouse” roles suggests potential gender imbalances in how income-earning is reported within households.

`race`

The dataset is overwhelmingly composed of White individuals (~85.4%). Other racial groups include Black (9.6%), Asian or Pacific Islander (3.2%), American Indian or Eskimo (1.0%), and Other (0.8%). The racial imbalance limits the generalizability of models trained on this data. Smaller racial groups may suffer from limited statistical power, affecting fairness and performance in predictive modeling.

`sex`

Males constitute 66.9% of the dataset, with females making up the remaining 33.1%. This male-skewed distribution could be due to sampling (e.g., primary earners in households), workforce participation patterns, or reporting biases.

`education_level`

Secondary-school graduates form the largest educational group (~32%), highlighting the central role of high school completion in the labor force. Tertiary education holders — those with university or equivalent degrees — account for nearly 25% of the population, representing a substantial segment with advanced qualifications. A notable 22.4% have attended some college without necessarily earning a degree, suggesting that partial post-secondary education is common, yet may not always translate into formal certification. The remaining 20% are distributed among those with only secondary education (9.4%), associate degrees (7.5%), primary school (3.5%), and a very small group with only preschool education (0.15%). It is ecident that the education distribution is skewed toward mid- to high-level education, with relatively few individuals having only basic schooling. This reflects a dataset that largely captures working-age adults in formal labor, which may underrepresent the least-educated populations.

`occupation_grouped`

White-collar occupations are the most prevalent (~51%), followed by blue-collar, service, and unknown. Smaller categories include military, which is marginal. Essentially, slightly over half of individuals in the dataset work in professional, managerial, sales, clerical, or tech-support roles. This suggests the dataset is heavily weighted toward professional and administrative occupations. Nearly a third of the population works in manual labor or skilled trade positions (craft, transport, machine operation, farming, etc.). This indicates a significant segment engaged in physically intensive or technical labor.

`native_region`

The vast majority of individuals are from North America (~92.3%). Smaller proportions are from Central America, Asia, Europe, South America, and a generic Other category. The heavy concentration of North American individuals reflects the U.S. focus of the dataset.

`age_group`

The largest groups are 26–35 and 36–45, followed by 46–60. These three age groups represent about 73% of the dataset. Very few individuals are under 18 or above 75, consistent with the dataset’s focus on the working-age population.

adult_df.describe(include='object')

	workclass	marital_status	relationship	race	sex	income	education_level	occupation_grouped	native_region	age_group
count	32513	32513	32513	32513	32513	32513	32513	32513	32513	32513
unique	8	4	5	5	2	2	7	5	6	7
top	private	married	male spouse	white	male	<=50k	secondary-high school graduate	white collar	north america	26-35
freq	22650	14984	13178	27771	21758	24677	10484	16532	30018	8501

adult_df['workclass'].value_counts(normalize=True)

workclass
private             0.696644
self-emp-not-inc    0.078123
government          0.069418
local-gov           0.064374
unknown             0.056470
self-employed       0.034325
voluntary           0.000431
unemployed          0.000215
Name: proportion, dtype: float64

adult_df['marital_status'].value_counts(normalize=True)

marital_status
married                  0.460862
single                   0.327684
divorced or separated    0.180912
widowed                  0.030542
Name: proportion, dtype: float64

adult_df['relationship'].value_counts(normalize=True)

relationship
male spouse          0.405315
single               0.360686
child                0.155599
female spouse        0.048227
extended relative    0.030173
Name: proportion, dtype: float64

adult_df['race'].value_counts(normalize=True)

race
white                        0.854151
black                        0.096023
asian or pacific islander    0.031926
american indian or eskimo    0.009565
other                        0.008335
Name: proportion, dtype: float64

Income Distribution

Given that `income` is the target variable, most of the analysis hereafter will be based on it. We first of all examine the income distribution in the dataset.

This pie chart visualizes the overall income split: 76% of individuals earn ≤50K, while 24% earn >50K. This means that nearly 3 out of 4 individuals fall into the lower income bracket (<=50K). This shows that there is a significant imbalance.

The bar chart visualizes the income distribution across age groups, using percentages within each group. There is an evident pattern in terms of income progression over the years with a gradual increase in terms of the number of people earning >50K starting from 0 amongst those aged 18 and below, peaking between 36 and 60 years, then declining after 60 years but not to zero.

All individuals under 18 earn <=50K, likely due to being students, minors, or ineligible for full-time employment. Extremely few young adults (2.1%) exceed 50K, as most are early in their careers, pursuing education, or in entry-level jobs. For the 26-35 age group, there’s a noticeable improvement — roughly 1 in 5 individuals in this group earn >50K, reflecting early career progression and accumulation of qualifications/experience. A substantial income increase is seen in the 36-45 age group: over a third now earn >50K. This is typically considered prime earning age where individuals settle into stable, higher-paying positions. Highest proportion of >50K earners is seen amongst individuals aged between 46 and 60— nearly 4 in 10. This reflects career maturity, peak seniority levels, and accumulated experience. There’s a drop-off in high incomes as many transition to retirement, part-time, or less demanding roles in the age group 61-75. Yet about 1 in 4 still earn >50K. Most in 76+ age group earn <=50K, likely due to retirement, pensions, or fixed incomes — but a small minority still earn higher incomes, possibly through continued work or investments.

Asia (30.7%) and Europe (29.2%) have the highest proportions of high-income earners. This suggests these immigrant groups might be better integrated into high-paying professional roles, or may represent a more skilled migrant profile in the dataset. Central America (11.1%) and South America (12.1%) have the lowest proportions of >50K earners. With 24.2% of North Americans earning >50K, this serves as a middle-ground baseline. Interestingly, both Asian and European groups outperform the native-born population proportionally in high-income brackets. The ‘Other’ group sits around 25.1%, close to North America’s rate. This likely reflects a diverse mix of regions not explicitly listed.

adult_df_income =adult_df.groupby('income').size().reset_index(name='total')
adult_df_income

	income	total
0	<=50k	24677
1	>50k	7836

pip install --upgrade plotly

Requirement already satisfied: plotly in c:\users\user\anaconda3\lib\site-packages (6.1.2)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\user\anaconda3\lib\site-packages (from plotly) (1.43.1)
Requirement already satisfied: packaging in c:\users\user\anaconda3\lib\site-packages (from plotly) (23.1)
Note: you may need to restart the kernel to use updated packages.

fig = px.pie(adult_df_income,
             names='income',
             values='total',
             title='Overall Income Distribution',
             color_discrete_sequence=px.colors.sequential.RdBu)

fig.update_layout(template="presentation",
                  paper_bgcolor="rgba(0,0,0,0)",
                  plot_bgcolor="rgba(0,0,0,0)") 

fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_pie_chart.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_pie_chart.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_pie_chart.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Income by Age Group

adult_df_income_age = adult_df.groupby(['age_group', 'income']).size().reset_index(name='total_by_age').sort_values(['age_group', 'income'])
adult_df_income_age

	age_group	income	total_by_age
0	18-25	<=50k	5333
1	18-25	>50k	114
2	26-35	<=50k	6910
3	26-35	>50k	1591
4	36-45	<=50k	5230
5	36-45	>50k	2771
6	46-60	<=50k	4479
7	46-60	>50k	2809
8	61-75	<=50k	1580
9	61-75	>50k	511
10	76+	<=50k	200
11	76+	>50k	40
12	<18	<=50k	945

total_per_group = adult_df_income_age.groupby('age_group').size()
total_per_group

age_group
18-25    2
26-35    2
36-45    2
46-60    2
61-75    2
76+      2
<18      1
dtype: int64

total_per_group = adult_df_income_age.groupby('age_group')['total_by_age'].transform('sum')
total_per_group

0     5447
1     5447
2     8501
3     8501
4     8001
5     8001
6     7288
7     7288
8     2091
9     2091
10     240
11     240
12     945
Name: total_by_age, dtype: int64

total_per_group = adult_df_income_age.groupby('age_group')['total_by_age'].transform('sum')
adult_df_income_age['percentage'] = (adult_df_income_age['total_by_age']/total_per_group) * 100
adult_df_income_age

	age_group	income	total_by_age	percentage
0	18-25	<=50k	5333	97.907105
1	18-25	>50k	114	2.092895
2	26-35	<=50k	6910	81.284555
3	26-35	>50k	1591	18.715445
4	36-45	<=50k	5230	65.366829
5	36-45	>50k	2771	34.633171
6	46-60	<=50k	4479	61.457190
7	46-60	>50k	2809	38.542810
8	61-75	<=50k	1580	75.561932
9	61-75	>50k	511	24.438068
10	76+	<=50k	200	83.333333
11	76+	>50k	40	16.666667
12	<18	<=50k	945	100.000000

fig = px.bar(
    adult_df_income_age,
    x='age_group',
    y='total_by_age',   
    color='income',
    title='Income Distribution by Age Group (%)',
    barmode='group',
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)
fig.update_traces(texttemplate='%{text..2f}%', textposition='outside'),
fig.update_layout(template="presentation", xaxis_title='Age Group',
                  yaxis_title='Percentage of population', legend_title=dict(text='Income Level'),
                  paper_bgcolor = "rgba(0, 0, 0, 0)", plot_bgcolor = "rgba(0, 0, 0, 0)")
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_agegroup_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

themes = ["plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white", "presentation", "xgridoff", "ygridoff", "gridon", "none"]

for theme in themes:
    fig.update_layout(template=theme)
    fig.show()

#pip install -U kaleido

#pip install -U plotly

Income by Native Region

adult_df_income_native_region = adult_df.groupby(['native_region', 'income']).size().reset_index(name='total_income_distribution')
adult_df_income_native_region

	native_region	income	total_income_distribution
0	asia	<=50k	465
1	asia	>50k	206
2	central america	<=50k	466
3	central america	>50k	58
4	europe	<=50k	369
5	europe	>50k	152
6	north america	<=50k	22768
7	north america	>50k	7250
8	other	<=50k	435
9	other	>50k	146
10	south america	<=50k	174
11	south america	>50k	24

total_per_group = adult_df_income_native_region.groupby('native_region').size()
total_per_group

native_region
asia               2
central america    2
europe             2
north america      2
other              2
south america      2
dtype: int64

total_per_group = adult_df_income_native_region.groupby('native_region')['total_income_distribution'].transform('sum')
total_per_group

0       671
1       671
2       524
3       524
4       521
5       521
6     30018
7     30018
8       581
9       581
10      198
11      198
Name: total_income_distribution, dtype: int64

total_per_group = adult_df_income_native_region.groupby('native_region')['total_income_distribution'].transform('sum')
adult_df_income_native_region['percentage'] = (adult_df_income_native_region['total_income_distribution']/total_per_group) * 100
adult_df_income_native_region

	native_region	income	total_income_distribution	percentage
0	asia	<=50k	465	69.299553
1	asia	>50k	206	30.700447
2	central america	<=50k	466	88.931298
3	central america	>50k	58	11.068702
4	europe	<=50k	369	70.825336
5	europe	>50k	152	29.174664
6	north america	<=50k	22768	75.847825
7	north america	>50k	7250	24.152175
8	other	<=50k	435	74.870912
9	other	>50k	146	25.129088
10	south america	<=50k	174	87.878788
11	south america	>50k	24	12.121212

fig = px.bar(
    adult_df_income_native_region,
    x='native_region',
    y='percentage',
    color='income',
    title='Income Distribution by Native Region (%)',
    barmode='group',
    height=500,
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)


fig.update_traces(
    texttemplate='%{text:.2f}%',
    textposition='outside'  
)

fig.update_layout(
    template="presentation",
    xaxis_title='Native Region',
    yaxis_title='Percentage of Population',
    legend_title_text='Income Level',
    xaxis_title_standoff=30,
    margin=dict(l=50, r=50, t=50, b=50)
)

fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Native Region_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Income by Race

adult_df_income_race = adult_df.groupby(['race', 'income']).size().reset_index(name='total_by_race')
adult_df_income_race

	race	income	total_by_race
0	american indian or eskimo	<=50k	275
1	american indian or eskimo	>50k	36
2	asian or pacific islander	<=50k	762
3	asian or pacific islander	>50k	276
4	black	<=50k	2735
5	black	>50k	387
6	other	<=50k	246
7	other	>50k	25
8	white	<=50k	20659
9	white	>50k	7112

total_per_group = adult_df_income_race.groupby('race').size()
total_per_group

race
american indian or eskimo    2
asian or pacific islander    2
black                        2
other                        2
white                        2
dtype: int64

total_per_group = adult_df_income_race.groupby('race')['total_by_race'].transform('sum')
total_per_group

0      311
1      311
2     1038
3     1038
4     3122
5     3122
6      271
7      271
8    27771
9    27771
Name: total_by_race, dtype: int64

total_per_group = adult_df_income_race.groupby('race')['total_by_race'].transform('sum')
adult_df_income_race['percentage'] = (adult_df_income_race['total_by_race']/total_per_group) * 100
adult_df_income_race

	race	income	total_by_race	percentage
0	american indian or eskimo	<=50k	275	88.424437
1	american indian or eskimo	>50k	36	11.575563
2	asian or pacific islander	<=50k	762	73.410405
3	asian or pacific islander	>50k	276	26.589595
4	black	<=50k	2735	87.604100
5	black	>50k	387	12.395900
6	other	<=50k	246	90.774908
7	other	>50k	25	9.225092
8	white	<=50k	20659	74.390551
9	white	>50k	7112	25.609449

fig = px.bar(
    adult_df_income_race,
    x = 'race',
    y = 'percentage',
    color = 'income',
    title='Income Distribution Per Race ',
    barmode='group',
    color_discrete_sequence=px.colors.sequential.RdBu,
    text='percentage'
)
fig.update_traces(texttemplate = '%{text:.2f}%')
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Race_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)

Education and occupation

adult_df_income_edu_occ = (adult_df.groupby(['education_level', 'occupation_grouped', 'income'])
                          .size().reset_index(name='total').sort_values('total', ascending = False))
adult_df_income_edu_occ

	education_level	occupation_grouped	income	total
44	secondary-high school graduate	blue collar	<=50k	3976
63	tertiary	white collar	>50k	3545
62	tertiary	white collar	<=50k	3369
32	same college	white collar	<=50k	3003
52	secondary-high school graduate	white collar	<=50k	2900
...	...	...	...	...
21	primary	unknow	>50k	4
14	preschool	white collar	<=50k	3
37	secondary	military	>50k	2
11	preschool	military	<=50k	2
19	primary	service	>50k	1

64 rows × 4 columns

adult_df_income_edu_occ['edu_occ'] = (adult_df_income_edu_occ['education_level'] + " | "
                                     + adult_df_income_edu_occ['occupation_grouped'])
adult_df_income_edu_occ

	education_level	occupation_grouped	income	total	edu_occ
44	secondary-high school graduate	blue collar	<=50k	3976	secondary-high school graduate | blue collar
63	tertiary	white collar	>50k	3545	tertiary | white collar
62	tertiary	white collar	<=50k	3369	tertiary | white collar
32	same college	white collar	<=50k	3003	same college | white collar
52	secondary-high school graduate	white collar	<=50k	2900	secondary-high school graduate | white collar
...	...	...	...	...	...
21	primary	unknow	>50k	4	primary | unknow
14	preschool	white collar	<=50k	3	preschool | white collar
37	secondary	military	>50k	2	secondary | military
11	preschool	military	<=50k	2	preschool | military
19	primary	service	>50k	1	primary | service

64 rows × 5 columns

num = 15
fig = px.bar(
    adult_df_income_edu_occ.head(15),
    x = 'total',
    y = 'edu_occ',
    color = 'income',
    orientation = 'h',
    title = f'Top{num} Education and Occupation Groups Combinations by Income Group',
    #barmode = 'group',
    height = 700,
    width=1100,
    color_discrete_sequence=px.colors.sequential.RdBu,
    text = 'total'
)

fig.update_layout(template="presentation", xaxis_title='Number of Individuals',
                  yaxis_title='Education | Occupation Group',
                  legend_title=dict(text='Income Level'),
                margin=dict(l=450, r=50, t= 50, b=50))
fig.update_traces(textposition='inside')
    
fig.show()
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.png'))
html_str = fig.to_html()

with open(os.path.join(results_dir, 'income_distribution_by_Education and Occupation_bar_plot.html'), 'w', encoding='utf-8') as f:
    f.write(html_str)